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Strictly less than half of the frequency<br /> | Strictly less than half of the frequency<br /> | ||
or no Sinusoidal component at that frequency<br /> | or no Sinusoidal component at that frequency<br /> | ||
| + | |||
| + | Though aliasing has already been mentioned briefly, it will be further explained here, along with its opposite, oversampling. Aliasing is the under-sampling of a signal. The main purpose of using Nyquist's Theorem is to eliminate any aliasing that may occur. As shown below on a simple sine wave signal in the time domain (time is on the x-axis), sampling with a sample rate less than twice the frequency of even just one of two signals could result in two signals appearing to be the same. | ||
| + | |||
Revision as of 17:49, 6 December 2020
Features of Nyquist's Theorem
Aliasing
Oversampling
Some say:
Strictly less than half of the frequency
or no Sinusoidal component at that frequency
Though aliasing has already been mentioned briefly, it will be further explained here, along with its opposite, oversampling. Aliasing is the under-sampling of a signal. The main purpose of using Nyquist's Theorem is to eliminate any aliasing that may occur. As shown below on a simple sine wave signal in the time domain (time is on the x-axis), sampling with a sample rate less than twice the frequency of even just one of two signals could result in two signals appearing to be the same.
